Classification of Movement EEG with Local Discriminant Bases

Abstract

We use local discriminant bases and linear discriminant analysis to classify EEG of left and right hand movement execution and imagination. The local discriminant bases adaptively segment and extract features from real and imagined movement EEG (2003 BCI competition) using cosine packets and Kullback-Leibler, Euclidean and Hellinger class separability (CS) criteria. We also tried principal component analysis (PCA) as another feature reduction method. In our case, CS ordered coefficients resulted in lower classification error than PCA using a smaller number of coefficients. We observed that the most discriminative components were located on the post movement beta and alpha synchronization. Pre-movement features were also selected by the algorithm. We believe that these segments correspond to the mental state and strategy of the subject during the movement execution/imagination. The main advantage of the algorithm is that it adaptively finds these physiological states in an ongoing EEG. This may eliminate the inter- and intra-subject variability. The average error rate of the classification was 12.7% for movement execution and 14.2% for movement imagination. Accordingly, the algorithm would be the 3rd best in the 2003 BCI (brain-computer interface) competition.